Machine learning, data engineering, and MLOps for engineers — Python for data analysis, ML fundamentals, RAG and vector databases, model deployment, and applied ML for predictive maintenance and forecasting.
Data science and machine-learning engineering have no government license — competence is shown through vendor and platform certifications. This is an overview of the certifications that matter for ML/data engineers and data scientists, what each covers, who runs it, and how to prepare.
AWS ML – Specialty prep: data engineering, modeling, tuning, and deploying/operating models on AWS (SageMaker).
GCP Professional ML Engineer prep: problem framing, Vertex AI pipelines, productionizing and monitoring models.
TensorFlow Developer Certificate prep: a hands-on coding exam — CNNs, NLP, sequences and time series in TF/Keras.
Databricks ML Associate/Professional prep: Spark ML, MLflow, scalable feature engineering and the model lifecycle.
Hands-on ML from fundamentals through deep learning, NLP, RAG, and LoRA fine-tuning — with 34 real Python code blocks from scikit-learn to LangChain.